AI Code Agent
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On-demand Webinar
On-demand Webinar

Context is the New Code: The Enterprise AI Blueprint | Harness Resource

Your team can build a flashy proof of concept by Friday, but when Monday comes and you need to scale across business units, integrate with legacy systems, and satisfy legal—that's when 90% of AI projects hit the wall.

This webinar delivers the four-pillar blueprint for AI that actually ships and scales:

  • Context: Stop prompt engineering. Start building organizational intelligence. Turn scattered data, tools, and institutional knowledge into a living knowledge graph that grounds every AI decision in truth, not hallucinations.
  • Evals: The unglamorous superpower. Build measurement systems that catch failures before users do, track what's regressing, and create feedback loops that make your AI systematically better.
  • Personalization: Generic AI assistants are forgettable. We'll show you how to make AI indispensable by teaching it your organization's language, role structures, and workflow logic.
  • Governance & Safety: The layer everyone punts on. We're tackling it head-on: compliance, privacy, and auditability when every prompt is unpredictable, and every LLM is a black box.

Walk away with: A practical framework to operationalize all four pillars—not theory, but tactics for moving from prototype to production-grade AI systems you can trust, govern, and evolve.

Who should attend: CTOs, heads of AI/data, product leaders, enterprise architects, and anyone in security or compliance who's tired of saying "not yet" to AI deployment.

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Published
December 11, 2025

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What you'll learn

Key Takeaways

Context Engineering Replaces Basic Prompting

While AI demos rely on simple prompt engineering, enterprise production requires robust context engineering. Building better systems is more important than simply using larger models.

Unify Scattered Data With Knowledge Graphs

Organizations should use knowledge graphs to connect siloed data sources like code and documentation. This provides grounded organizational intelligence for AI agents to query.

Enforce Strict Access Controls for AI

A major risk in scaling AI is broken access controls. AI systems must respect user identity and permissions to prevent the exposure of sensitive knowledge.

Implement Real-Time Policy Enforcement

Enterprise AI requires pre-validation and post-validation of LLM requests to ensure outputs adhere to company policies. Teams must also version control and log all prompts and models for auditing.

Centralize AI Architecture Across Teams

A unified AI team should lead the effort to build organizational intelligence. This prevents fragmented and inconsistent AI behaviors across different product lines.